A. Field of the Invention
The present invention pertains generally to automated methods of measuring atmospheric visibility and, more particularly, to a video camera-based system for measuring atmospheric visibility.
B. Description of the Background
Visibility conditions are affected by the structure and elements of the atmosphere, such as fog, snow, wind, dust, and other adverse conditions. Visibility has been defined as the greatest distance at which an object of specified characteristics can be see and detected by the naked eye. See American Meteorological Society, Glossary of Meteorology, 1989, and W. E. Knowles Middleton, Vision Through Atmosphere, 1968, University of Toronto Press. Atmospheric visibility is normally expressed in distance, such as meters or yards, or at greater distances in kilometers or miles. Atmospheric visibility at night is determined by measuring the distance from a point of light of a given intensity to a point where the light source is just detectable by the human eye. See American Meteorological Society and W. E. Knowles Middleton, supra. Currently, most visibility measurement instruments are based on the principle of measuring forward or backward light scattering effects.
As light is scattered by atmospheric particles, scattered light meters record the visibility. There are a number of problems, however, with measuring visibility using scattered light meters (SLMs). First, light scattering by atmospheric particles such as atmospheric moisture is only one of the effects that reduces visibility in the atmosphere. For example, absorption or obscuration by large particulate matter such as snow, dust, etc. can have a much greater effect on atmospheric visibility than simple light scattering of smaller particles, such as atmospheric moisture that causes fog. Hence, light scattering of smaller particles only contributes partially to atmospheric visibility effects.
Additionally, SLMs typically only measure the light scattering effects of a small region of a few cubic inches located adjacent the SLM. If the local region adjacent the SLM deviates from the overall visibility, such as the visibility along a road at a distance of up to 300 meters, the SLM will report a very large error. Local regions of fog, blowing snow, local dust storms and other effects can cause the visibility to vary drastically from one spot to another. Hence, SLMs are prone to providing incorrect readings of visibility in adverse conditions.
While SLMs provide a fairly accurate measure of light scattering, and hence visibility under conditions of fog where atmospheric moisture is the primary contributing factor for low visibility, SLMs may provide a high margin of error for visibility measurements under rain and snow conditions for the reasons as set forth above. One of the reasons is that there is a less significant correlation between atmospheric visibility and the light scattering effect under rain and snow conditions. Additionally, the light scattering effect varies with different types of atmospheric particles. In order for SLMs to correctly measure visibility, the SLMs need to recognize both the types and size of particles and self-calibrate to adjust the measurement of atmospheric visibility according to the different scattering properties of atmospheric particles to provide a proper measurement. The ability to determine the types and sizes of particles present in the atmosphere, as well as the ability to self-calibrate an SLM according to the types and sizes of particles detected, would greatly increase the cost of the SLM and would most likely provide results that are still prone to errors.
Hence, atmospheric visibility that is perceived by the human eye can often be very different from the visual range measured by SLMs due to the basic operating principles of the SLM. As pointed out above, SLMs only measure one physical property of the atmosphere, and only measure that property in a small region that is located near the SLM. Additionally, SLMs do not provide an easy way to verify the correctness of the visibility that is reported by the SLM. Since visibility is often used to make critical decisions in transportation applications, such as road closures or speed limit decisions, it is vitally important that such decisions be verified.
Statistical reliability has been used as an alternative to verification in some instances. For example, several SLMs may be installed in the area in which atmospheric visibility is to be measured to increase the statistical reliability of the measurements that are made. Since SLMs are very expensive, this greatly increases the cost, and the SLMs still suffer from the inherent problems indicated above. Moreover, SLMs require a high degree of precision in the alignment of the transmitter and receiver optics which additionally adds to the cost of the SLM systems.
It is against this background, and the limitations and problems associated therewith, that the present invention has been developed.